Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	129,249
2	South Dakota	124,418
3	Rhode Island	114,050
4	Utah	112,178
5	Arizona	109,115
6	Tennessee	108,358
7	Iowa	104,138
8	Wisconsin	104,043
9	Oklahoma	103,821
10	Arkansas	103,256
11	Nebraska	101,166
12	Kansas	99,271
13	Alabama	97,623
14	Indiana	96,492
15	Mississippi	95,979
16	Idaho	93,645
17	South Carolina	93,257
18	Nevada	93,210
19	Wyoming	91,723
20	Illinois	91,636
21	Montana	90,906
22	Louisiana	90,041
23	Texas	88,160
24	Georgia	88,050
25	California	87,817
26	Kentucky	87,435
27	New Mexico	85,712
28	Delaware	84,479
29	Florida	84,478
30	Minnesota	83,679
31	New Jersey	83,319
32	Missouri	82,706
33	Massachusetts	80,349
34	Ohio	79,966
35	New York	78,291
36	North Carolina	78,111
37	Alaska	76,561
38	Connecticut	74,983
39	Colorado	71,920
40	West Virginia	70,801
41	Pennsylvania	69,782
42	Virginia	63,758
43	Michigan	62,839
44	Maryland	61,061
45	District of Columbia	54,792
46	New Hampshire	51,534
47	Washington	43,392
48	Puerto Rico	40,873
49	Oregon	35,463
50	Maine	31,437
51	Vermont	21,498
52	Hawaii	18,837

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Carolina	637
2	New Jersey	441
3	Rhode Island	436
4	New York	433
5	Texas	429
6	Kentucky	423
7	Oklahoma	400
8	Virginia	394
9	North Carolina	389
10	Utah	374
11	Florida	367
12	Delaware	360
13	Kansas	359
14	Georgia	355
15	Tennessee	342
16	Massachusetts	334
17	Arkansas	304
18	Louisiana	303
19	Mississippi	300
20	New Hampshire	282
21	Alabama	272
22	Pennsylvania	269
23	Ohio	267
24	Colorado	261
25	Arizona	260
26	Connecticut	255
27	Iowa	255
28	West Virginia	253
29	South Dakota	249
30	Montana	245
31	California	241
32	Alaska	231
33	New Mexico	229
34	Indiana	226
35	Nebraska	219
36	Illinois	217
37	Nevada	207
38	Idaho	206
39	Wisconsin	202
40	Maryland	190
41	District of Columbia	183
42	Missouri	177
43	Vermont	165
44	Maine	155
45	Minnesota	153
46	Wyoming	147
47	Michigan	140
48	Washington	132
49	Oregon	129
50	North Dakota	127
51	Puerto Rico	54
52	Hawaii	50

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,521
2	New York	2,320
3	Massachusetts	2,228
4	Rhode Island	2,161
5	Mississippi	2,160
6	Connecticut	2,070
7	South Dakota	2,069
8	Arizona	2,039
9	Louisiana	1,995
10	North Dakota	1,917
11	Alabama	1,872
12	Indiana	1,799
13	Pennsylvania	1,797
14	Illinois	1,738
15	Arkansas	1,727
16	New Mexico	1,671
17	Iowa	1,655
18	Michigan	1,603
19	Tennessee	1,581
20	South Carolina	1,533
21	Nevada	1,515
22	Kansas	1,497
23	Georgia	1,435
24	Texas	1,416
25	District of Columbia	1,382
26	Florida	1,329
27	Delaware	1,303
28	Ohio	1,294
29	Missouri	1,277
30	Maryland	1,241
31	Montana	1,239
32	West Virginia	1,227
33	California	1,175
34	Wisconsin	1,157
35	Minnesota	1,140
36	Wyoming	1,117
37	Nebraska	1,090
38	Colorado	1,023
39	Idaho	1,007
40	Oklahoma	1,000
41	North Carolina	992
42	Kentucky	989
43	New Hampshire	828
44	Virginia	816
45	Washington	622
46	Puerto Rico	597
47	Utah	557
48	Oregon	500
49	Maine	478
50	Alaska	370
51	Vermont	302
52	Hawaii	298

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Ohio	95
2	Alabama	40
3	Arizona	24
4	Kansas	19
5	Delaware	16
6	Missouri	13
7	Rhode Island	13
8	South Carolina	13
9	Tennessee	13
10	California	12
11	Georgia	12
12	Texas	12
13	Massachusetts	11
14	Nevada	11
15	New Mexico	11
16	New Jersey	10
17	North Carolina	10
18	Mississippi	9
19	Pennsylvania	9
20	West Virginia	9
21	Florida	8
22	Indiana	8
23	Iowa	8
24	Kentucky	8
25	Louisiana	8
26	South Dakota	8
27	Arkansas	7
28	Connecticut	7
29	District of Columbia	7
30	New York	7
31	Oklahoma	7
32	Illinois	5
33	Maryland	4
34	Nebraska	4
35	New Hampshire	4
36	Oregon	4
37	Washington	4
38	Idaho	3
39	Michigan	3
40	Minnesota	3
41	Utah	3
42	Wisconsin	3
43	Colorado	2
44	Montana	2
45	Virginia	2
46	Hawaii	1
47	North Dakota	1
48	Puerto Rico	1
49	Vermont	1
50	Alaska	0
51	Maine	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	316,449	1	99
Chattahoochee	Georgia	265,242	2	99
Bent	Colorado	251,031	3	99
Lincoln	Arkansas	239,327	4	99
Lake	Tennessee	236,317	5	99
Davidson	Tennessee	126,014	227	92
Richland	South Carolina	92,003	1275	59
York	South Carolina	85,665	1595	49
Orange	California	80,377	1839	41
Pierce	Washington	40,612	2902	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Iron	Wisconsin	6,682	3	99
Buffalo	South Dakota	6,626	4	99
Dickey	North Dakota	6,568	5	99
Davidson	Tennessee	1,161	1921	38
Richland	South Carolina	1,109	1982	36
Orange	California	1,100	1991	36
York	South Carolina	1,032	2090	33
Pierce	Washington	560	2679	14

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.